TNF Family-Based Signature Predicts Prognosis, Tumor Microenvironment, and Molecular Subtypes in Bladder Carcinoma

Establish TIIC signature score based the machine learning fusion in bladder cancer

BACKGROUND

Bladder cancer is a prevalent malignant tumor with high heterogeneity. Current treatments, such as transurethral resection of bladder tumor (TURBT) and intravesical Bacillus Calmette-Guérin (BCG) therapy, still have limitations, with approximately 30% of non-muscle-invasive bladder cancer (NMIBC) progressing to muscle-invasive bladder cancer (MIBC), and a substantial number of MIBC patients experiencing recurrence after surgery. Immunotherapy has shown potential benefits, but accurate prediction of its prognostic effects remains challenging.

METHODS

We analyzed bladder cancer RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and used various machine learning algorithms to screen for feature RNAs related to tumor-infiltrating immune cells (TIICs) from single-cell data. Based on these RNAs, we established a TIIC signature score and evaluated its relationship with overall survival (OS) and immunotherapy response in bladder cancer patients.

RESULTS

The study identified 171 TIIC-RNAs and selected 11 TIIC-RNAs with prognostic value through survival analysis. The TIIC signature score established using a machine learning fusion method was significantly associated with OS and showed good predictive performance in different datasets. Additionally, the signature score was negatively correlated with immunotherapy response, with patients with low TIIC feature scores showing better survival outcomes after immunotherapy. Further biological functional analysis revealed a close association between the TIIC signature score and immune regulation processes, cellular metabolism, and genetic variations.

CONCLUSION

This study successfully constructed and validated an RNA signature scoring system based on tumor-infiltrating immune cell (TIIC) features, which can effectively predict OS and the effectiveness of immunotherapy in bladder cancer patients.

Results from a real-world study: a novel glycosyltransferase risk score for prognosis, tumor microenvironment phenotypes and immunotherapy in bladder cancer

BACKGROUND

Although immunotherapy shows tremendous potential in the treatment of bladder cancer (BLCA), the overall prognosis and response rates to immunotherapy in BLCA remain suboptimal.

METHODS

We performed an extensive evaluation of glycosyltransferase expression patterns in BLCA patients by analyzing 210 glycosyltransferase-related genes. Subsequently, we established correlations between these glycosyltransferase patterns, prognosis, and tumor microenvironment (TME) phenotypes. To offer personalized patient assessments, we developed a glycosyltransferase risk score that accurately predicts prognosis, TME phenotypes, and molecular subtypes. Importantly, we developed a RNA-seq cohort, named Xiangya cohort, to validate our results.

RESULTS

Two distinct patterns of glycosyltransferase expression were identified, corresponding to inflamed and noninflamed TME phenotypes, and demonstrated the potential to predict prognosis. We developed and validated a comprehensive risk score that accurately predicted individual patient prognosis in the TCGA-BLCA cohort. Additionally, we constructed a nomogram that integrated the risk score with several key clinical factors. Importantly, this risk score was successfully validated in external cohorts, including the Xiangya cohort and GSE48075. Furthermore, we discovered a positive correlation between this risk score and tumor-infiltrating lymphocytes in both the TCGA-BLCA and Xiangya cohorts, suggesting that patients with a higher risk score exhibited an inflamed TME phenotype and were more responsive to immunotherapy. Finally, we observed that the high and low risk score groups were consistent with the luminal and basal subtypes of BLCA, respectively, providing further validation of the risk score's role in the TME in terms of molecular subtypes.

CONCLUSIONS

Glycosyltransferase patterns exhibit distinct TME phenotypes in BLCA. Our comprehensive risk score provides a promising approach for prognostic prediction and assessment of immunotherapy efficacy, offering valuable guidance for precision medicine.

Identifying Factors Affecting the Survival of Patients with HIV-Associated B-Cell Lymphoma Using a Random Survival Forest Model

Background

There have been no reports about the application of random survival forest (RSF) model to predict disease progression of HIV-associated B-cell lymphoma.

Methods

A total of 44 patients with HIV-associated B-cell lymphoma who were referred to Nanjing Second Hospital from 2012 to 2019 were included. The RSF model was used to find predictors of survival, and the results of the RSF model were compared with those of the Cox model. The data were analyzed using R software (version 4.1.1).

Results

One-, 2-, and 3-year survival rates were 74.5%, 57.7%, and 48.6%, respectively, and the median survival was 59.0 months. The first 3 most important predictors of survival included lactate dehydrogenase (LDH), absolute monocyte count (AMC), and white blood cells (WBCs) count. The median survival of high-risk patients was only 4.0 months. Areas under the curve (AUCs) of the RSF model remained at more than 0.90 at 1, 2, and 3 years. The RSF model displayed a lower prediction error rate (21.9%) than the Cox model (25.4%).

Conclusions

Lactate dehydrogenase, AMC, and WBCs count are the most important prognostic predictors for patients with HIV-associated B-cell lymphoma. Much larger prospective and/or multicentre studies are required to validtae this RSF model.

Exploring the Targets and Molecular Mechanisms of Curcumin for the Treatment of Bladder Cancer Based on Network Pharmacology, Molecular Docking and Molecular Dynamics

Curcumin, a phenolic compound derived from turmeric, has demonstrated anti-tumor properties in preclinical models of various cancers. However, the exact mechanism of curcumin in treating bladder cancer remains unclear. This study aimed to elucidate the therapeutic targets and molecular mechanisms of curcumin in the treatment of BC through an integrated approach of network pharmacology, molecular docking, and molecular dynamics simulations. PharmMapper, SuperPred, TargetNet, and SwissTargetPrediction were utilized to acquire targets associated with curcumin, while GeneCards, CTD, DisGeNET, OMIM, and PharmGKB databases were utilized to obtain targets related to bladder cancer. The drug-disease interaction targets were obtained using Venny 2.1.0, and GO and KEGG enrichment analyses were then conducted with the DAVID tool. We constructed a protein-protein interaction (PPI) network and identified tenkey targets. In conclusion, AutoDock Tools 1.5.7 was utilized to conduct molecular docking simulations, followed by additional analysis of the central targets through the GEPIA, HPA, cBioPortal, and TIMER databases. A total of 305 potential anticancer targets of curcumin were obtained. The analysis of GO functional enrichment resulted in a total of 1105 terms, including 786 terms related to biological processes (BP), 105 terms related to cellular components (CC), and 214 terms related to molecular functions (MF). In addition, KEGG pathway enrichment analysis identified 170 relevant signaling pathways. Treating bladder cancer could potentially involve inhibiting pathways like the PI3K-Akt signaling pathway, MAPK signaling pathway, EGFR tyrosine kinase inhibitor resistance, and IL-17 signaling pathway. Activating TNF, ALB, CASP3, and ESR1 while inhibiting AKT1, EGFR, STAT3, BCL2, SRC, and HSP90AA1 can also hinder the proliferation of bladder tumor cells. According to the results of molecular docking, curcumin binds to these central targets in a spontaneous manner, exhibiting binding energies lower than - 1.631 kJ/mol. These findings were further validated at the transcriptional, translational and immune infiltration levels. By utilizing network pharmacology and molecular docking techniques, it was discovered that curcumin possesses diverse effects on multiple targets and pathways for treating bladder cancer. It has the potential to impede the growth of bladder tumor cells by suppressing various pathways including the PI3K-Akt and MAPK signaling pathways, as well as pathways associated with EGFR tyrosine kinase inhibitor resistance and the IL-17 signaling pathway. Curcumin could potentially disrupt the cell cycle advancement in bladder cancer cells by increasing the expression of TNF, ALB, CASP3, and ESR1 while decreasing AKT1, EGFR, STAT3, BCL2, SRC, HSP90AA1, and other targeted genes. These findings reveal the possible molecular pathways through which curcumin exerts its anticancer effects in bladder cancer, and this novel research strategy not only provides an important basis for an in-depth understanding of the anticancer mechanism of curcumin, but also offers new potential drugs and targets for the clinical treatment of bladder cancer. Therefore, this study is of great scientific significance and practical application value for promoting the development of bladder cancer therapeutic field. This finding provides strong support for the development of novel, safe and effective drugs for bladder cancer treatment.

Identification and validation of obesity related genes signature based on microenvironment phenotypes in prostate adenocarcinoma

BACKGROUND

The role of obesity related genes (ORGs) in the immune checkpoint inhibitors (ICIs) treatment of prostate adenocarcinoma (PRAD) has not yet been proved by research.

METHODS

We comprehensively evaluated the ORGs patterns in PRAD based on tumor microenvironment (TME) phenotypes and immunotherapy efficacies. Then we constructed a ORGs risk score for prognosis and a ORGs signature for accurate prediction of TME phenotype and immunotherapy efficacy in order to evaluate individual patients.

RESULTS

Two distinct ORGs patterns were generated. The two ORGs patterns were consistent with inflammatory and non-inflammatory TME phenotypes. ORGs patterns had an important role for predicting immunotherapy efficacies. Next, we constructed a ORGs risk score for predicting each patient's prognosis with high performance in TCGA-PRAD. The ORGs risk score could be well verified in the external cohorts including GSE70769 and GSE21034. Then, we developed a ORGs signature and found it was significantly positively correlated with tumor-infiltrating lymphocytes in TCGA-PRAD. We found that each patient in the high-risk ORGs signature group represented a non-inflamed TME phenotype on the single cell level. The patients with high ORGs signature had more sensitivity to immunotherapy. And those ORGs were verified.

CONCLUSIONS

ORGs pattern depicts different TME phenotypes in PRAD. The ORGs risk score and ORGs signature have an important role for predicting prognosis and immunotherapy efficacies.

A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis

Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited. Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts. Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p < 0.001) and used it to construct an accurate prognostic prediction nomogram. The high glycosylation risk score group exhibited higher tumor immune cell infiltration, enrichment scores in immune therapy-related pathways, and a tendency towards a basal subtype. Conversely, the low-risk score group had minimal immune cell infiltration and tended to have a luminal subtype. These findings were consistent in our real-world Xiangya cohort. Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.

Screening of immunotherapy-related genes in bladder cancer based on GEO datasets

Background

As one of the most prevalent genitourinary cancers, bladder cancer (BLCA) is associated with high morbidity and mortality. Currently, limited indicators are available for early detection and diagnosis of bladder cancer, and there is a lack of specific biomarkers for evaluating the prognosis of BLCA patients. This study aims to identify critical genes that affect bladder cancer immunity to improve the diagnosis and prognosis of bladder cancer and to identify new biomarkers and targets for immunotherapy.

Methods

Two GEO datasets were used to screen differentially expressed genes (DEGs). The STRING database was used to construct a protein-protein interaction network of DEGs, and plug-in APP CytoHubba in Cytoscape was used to identify critical genes in the network. GO and KEGG analyses explored the functions and pathways of differential gene enrichment. We used GEPIA to validate the expression of differential genes, their impact on patient survival, and their relationship to clinicopathological parameters. Additionally, hub genes were verified using qRT-PCR and Western blotting. Immune infiltration analysis and multiple immunohistochemistry reveal the impact of Hub genes on the tumor microenvironment.

Result

We screened out 259 differential genes, and identified 10 key hub genes by the degree algorithm. Four genes (ACTA2, FLNA, TAGLN, and TPM1) were associated with overall or disease-free survival in BLCA patients and were significantly associated with clinical parameters. We experimentally confirmed that the mRNA and protein levels of these four genes were significantly decreased in bladder cancer cells. Immunoassays revealed that these four genes affect immune cell infiltration in the tumor microenvironment; they increased the polarization of M2 macrophages.

Conclusion

These four genes affect the tumor microenvironment of bladder cancer, provide a new direction for tumor immunotherapy, and have significant potential in the diagnosis and prognosis of bladder cancer.

A novel platelet risk score for stratifing the tumor immunophenotypes, treatment responses and prognosis in bladder carcinoma: results from real-world cohorts

Background: Although the durable efficacy of immune checkpoint inhibitors (ICIs) in BLCA has been confirmed in numerous studies, not all patients benefit from their application in the clinic. Platelets are increasingly being found to be closely associated with cancer progression and metastasis; however, their comprehensive role in BLCA remains unclear. Methods: We comprehensively explored platelet expression patterns in BLCA patients using an integrated set of 244 related genes. Correlations between these platelet patterns with tumor microenvironment (TME) subtypes, immune characteristics and immunotherapy efficacies were explored. In addition, a platelet risk score (PRS) was generated for individual prognosis and verified the ability to predict prognosis, precise TME phenotypes, and immunotherapy efficacies. Results: Genes were clustered into two patterns that represented different TME phenotypes and had the ability to predict immunotherapy efficacy. We constructed a PRS that could predict individual prognosis with satisfactory accuracy using TCGA-BLCA. The results remained consistent when PRS was validated in the GSE32894 and Xiangya cohort. Moreover, we found that our PRS was positively related to tumor-infiltrating lymphocytes (TILs) in the TCGA-BLCA and Xiangya cohort. As expected, patients with higher PRS exhibited more sensitive to immunotherapy than patients with lower PRS. Finally, we discovered that a high PRS indicated a basal subtype of BLCA, whereas a low PRS indicated a luminal subtype. Conclusion: Platelet-related genes could predict TME phenotypes in BLCA. We constructed a PRS that could predict the TME, prognosis, immunotherapy efficacy, and molecular subtypes in BLCA.

Identification of molecular subtypes based on liquid-liquid phase separation and cross-talk with immunological phenotype in bladder cancer

Background

Mounting evidence has demonstrated that an imbalance in liquid-liquid phase separation (LLPS) can induce alteration in the spatiotemporal coordination of biomolecular condensates, which plays a role in carcinogenesis and cachexia. However, the role of LLPS in the occurrence and progression of bladder cancer (BLCA) remains to be elucidated. Identifying the role of LLPS in carcinogenesis may aid in cancer therapeutics.

Methods

A total of 1,351 BLCA samples from six cohorts were retrieved from publicly available databases like The Cancer Genome Atlas, Gene Expression Omnibus, and ArrayExpress. The samples were divided into three distinct clusters, and their multi-dimensional heterogeneities were explored. The LLPS patterns of all patients were determined based on the LLPS-related risk score (LLPSRS), and its multifaceted landscape was depicted and experimentally validated at the multi-omics level. Finally, a cytotoxicity-related and LLPSRS-based classifier was established to predict the patient's response to immune checkpoint blockade (ICB) treatment.

Results

Three LLPS-related subtypes were identified and validated. The differences in prognosis, tumor microenvironment (TME) features, cancer hallmarks, and certain signatures of the three LLPS-related subtypes were validated. LLPSRS was calculated, which could be used as a prognostic biomarker. A close correlation was observed between clinicopathological features, genomic variations, biological mechanisms, immune infiltration in TME, chemosensitivity, and LLPSRS. Furthermore, our classifier could effectively predict immunotherapy response in patients with BLCA.

Conclusions

Our study identified a novel categorization of BLCA patients based on LLPS. The LLPSRS could predict the prognosis of patients and aid in designing personalized medicine. Further, our binary classifier could effectively predict patients' sensitivity to immunotherapy.

Identification and characterization of aging/senescence-induced genes in osteosarcoma and predicting clinical prognosis

Background

Aging is an influential risk factor for progression of both degenerative and oncological diseases of the bone. Osteosarcoma, considered the most common primary mesenchymal tumor of the bone, is a worldwide disease with poor 5-year survival. This study investigated the role of aging-/senescence-induced genes (ASIGs) in contributing to osteosarcoma diagnosis, prognosis, and therapeutic agent prediction.

Methods

Therapeutically Applicable Research to Generate Effective Treatments (TARGET), Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) were used to collect relevant gene expression and clinical data of osteosarcoma and paracancerous tissues. Patients were clustered by consensus using prognosis-related ASIGs. ssGSEA, ESTIMATE, and TIMER were used to determine the tumor immune microenvironment (TIME) of subgroups. Functional analysis of differentially expressed genes between subgroups, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set variation analyses (GSVAs), was performed to clarify functional status. Prognostic risk models were constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. SCISSOR was used to identify relevant cells in osteosarcoma single-cell data for different risk groups. The effect of immunotherapy was predicted based on TIDE scores and chemotherapy drug sensitivity using CTRP and PRISM.

Results

Three molecular subgroups were identified based on prognostic differentially expressed ASIGs. Immunological infiltration levels of the three groups differed significantly. Based on GO and KEGG analyses, differentially expressed genes between the three subgroups mainly relate to immune and aging regulation pathways; GSVA showed substantial variations in multiple Hallmark pathways among the subgroups. The ASIG risk score built based on differentially expressed genes can predict patient survival and immune status. We also developed a nomogram graph to accurately predict prognosis in combination with clinical characteristics. The correlation between the immune activation profile of patients and the risk score is discussed. Through single-cell analysis of the tumor microenvironment, we identified distinct risk-group-associated cells with significant differences in immune signaling pathways. Immunotherapeutic efficacy and chemotherapeutic agent screening were evaluated based on risk score.

Conclusion

Aging-related prognostic genes can distinguish osteosarcoma molecular subgroups. Our novel aging-associated gene signature risk score can be used to predict the osteosarcoma immune landscape and prognosis. Moreover, the risk score correlates with the TIME and provides a reference for immunotherapy and chemotherapy in terms of osteosarcoma.

Cuproptosis-related modification patterns depict the tumor microenvironment, precision immunotherapy, and prognosis of kidney renal clear cell carcinoma

Background

Due to the different infiltration abundance of immune cells in tumor, the efficacy of immunotherapy varies widely among individuals. Recently, growing evidence suggested that cuproptosis has impact on cancer immunity profoundly. However, the comprehensive roles of cuproptosis-related genes in tumor microenvironment (TME) and in response to immunotherapy are still unclear.

Methods

Based on 43 cuproptosis-related genes, we employed unsupervised clustering to identify cuproptosis-related patterns and single-sample gene set enrichment analysis algorithm to build a cuproptosis signature for individual patient's immune cell infiltration and efficacy of immune checkpoint blockade (ICB) evaluation. Then, the cuproptosis-related genes were narrowed down using univariate Cox regression model and least absolute shrinkage and selection operator algorithm. Finally, a cuproptosis risk score was built by random survival forest based on these narrowed-down genes.

Results

Two distinct cuproptosis-related patterns were developed, with cuproptosis cluster 1 showing better prognosis and higher enrichment of immune-related pathways and infiltration of immune cells. For individual evaluation, the cuproptosis signature that we built could be used not only for predicting immune cell infiltration in TME but also for evaluating an individual's sensitivity to ICBs. Patients with higher cuproptosis signature scores exhibited more activated cancer immune processes, higher immune cell infiltration, and better curative efficacy of ICBs. Furthermore, a robust cuproptosis risk score indicated that patients with higher risk scores showed worse survival outcomes, which could be validated in internal and external validation cohorts. Ultimately, a nomogram which combined the risk score with the prognostic clinical factors was developed, and it showed excellent prediction accuracy for survival outcomes.

Conclusion

Distinct cuproptosis-related patterns have significant differences on prognosis and immune cell infiltration in kidney renal clear cell carcinoma (KIRC). Cuproptosis signature and risk score are able to provide guidance for precision therapy and accurate prognosis prediction for patients with KIRC.

An EMT-based risk score thoroughly predicts the clinical prognosis, tumor immune microenvironment and molecular subtypes of bladder cancer

Background

Epithelial mesenchymal transition (EMT) is closely related to the occurrence, development, metastasis and antitumor immunity of tumors. However, comprehensive studies correlating EMT and prognosis, tumor microenvironment (TME) and molecular subtypes of bladder cancer (BLCA) are lacking.

Methods

TCGA-BLCA was chosen as our training cohort, while Xiangya cohort, GSE13507, GSE48075 were selected as our validation cohorts. Prognostic genes were screened out using univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Then we developed an EMT risk score based on these prognostic genes and systematically correlated the risk score with prognosis, TME and molecular subtypes of BLCA.

Results

Based on EMT related genes, we developed two different EMT patterns, named EMT cluster 1 and cluster 2, and found that cluster 2 showed a worse prognosis and an inflammatory TME phenotype. For personalized prognosis and TME phenotypes predicting, we developed and validated an EMT-based risk score by 7 candidate genes (ANXA10, CNTN1, FAM180A, FN1, IGFL2, KANK4 and TOX3). Patients with high EMT risk scores had lower overall survival (OS) with high predictive accuracy both in the training cohort and validation cohort. In addition, we comprehensively correlated the EMT risk score with TME and molecular subtype, and found that high EMT risk score suggested higher levels of immune cell infiltration and more inclined to present the basal molecular subtype. It was noteworthy that the same results also appeared in the validation of Xiangya cohort.

Conclusions

EMT related genes play an important role in tumor progression and immunity in BLCA. Our EMT risk score could accurately predict prognosis and immunophenotype of a single patient, which could guide more effective precision medical strategies.

Cuproptosis depicts tumor microenvironment phenotypes and predicts precision immunotherapy and prognosis in bladder carcinoma

Background

Though immune checkpoint inhibitors (ICIs) exhibit durable efficacy in bladder carcinomas (BLCAs), there are still a large portion of patients insensitive to ICIs treatment.

Methods

We systematically evaluated the cuproptosis patterns in BLCA patients based on 46 cuproptosis related genes and correlated these cuproptosis patterns with tumor microenvironment (TME) phenotypes and immunotherapy efficacies. Then, for individual patient's evaluation, we constructed a cuproptosis risk score (CRS) for prognosis and a cuproptosis signature for precise TME phenotypes and immunotherapy efficacies predicting.

Results

Two distinct cuproptosis patterns were generated. These two patterns were consistent with inflamed and noninflamed TME phenotypes and had potential role for predicting immunotherapy efficacies. We constructed a CRS for predicting individual patient's prognosis with high accuracy in TCGA-BLCA. Importantly, this CRS could be well validated in external cohorts including GSE32894 and GSE13507. Then, we developed a cuproptosis signature and found it was significantly negative correlated with tumor-infiltrating lymphocytes (TILs) both in TCGA-BLCA and Xiangya cohorts. Moreover, we revealed that patients in the high cuproptosis signature group represented a noninflamed TME phenotype on the single cell level. As expected, patients in the high cuproptosis signature group showed less sensitive to immunotherapy. Finally, we found that the high and low cuproptosis signature groups were consistent with luminal and basal subtypes of BLCA respectively, which validated the role of signature in TME in terms of molecular subtypes.

Conclusions

Cuproptosis patterns depict different TME phenotypes in BLCA. Our CRS and cuproptosis signature have potential role for predicting prognosis and immunotherapy efficacy, which might guide precise medicine.

Network Pharmacology-Integrated Molecular Docking Reveals the Expected Anticancer Mechanism of Picrorhizae Rhizoma Extract

This study sought to explore the anticancer mechanism of Picrorhizae Rhizoma (PR) extract based on network pharmacology and molecular docking. The potential chemicals of PR were screened through the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and relevant literatures. Corresponding targets of active ingredients were found with the help of the UniProtKB database, and therapeutic targets for cancer action were screened with the help of the GeneCards database. We used Cytoscape software to construct the compound-target-pathway network of PR extract. We utilized the STRING database to obtain the protein-protein interaction (PPI) network. We used DAVID database combining Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, molecular docking was employed for initial efficacy checking. We have identified 16 potential active components of PR through screening, involving 112 disease action targets. Utilizing the GeneCards database, 112 intersecting targets between PR extract and cancer were found, which mainly exerts anticancer effects by regulating tumor necrosis factor (TNF), recombinant caspase 3 (CASP3), c-Jun NH2-terminal kinase (JNK)/JUN, epidermal growth factor receptor (EGFR), and estrogen receptor-1 (ESR1) with some other target genes and pathways associated with cancer. The major anticancer species are prostate cancer, colorectal cancer, small cell lung cancer, etc. In the molecular docking study, herbactin had a strong affinity for TNF. Based on network pharmacology and molecular docking studies, PR and their compounds have demonstrated potential anticancer activities against several key targets. Our preliminary findings provide a strong foundation for further experiments with PR constituents.